{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The prediction task is to determine whether a person makes over $50K a year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('output.csv')\n",
    "y = data['counts']\n",
    "x = data['workclass']\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.bar(x, y)\n",
    "plt.xlabel('workclass', fontsize=10)\n",
    "plt.ylabel('counts', fontsize=10)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('census_income')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
